Four important lessons to help allocate your media budget

Optimizing media budgets can be quite a challenge in today’s omnichannel world. Increasing amounts of information become available to marketers, who are expected to take all these factors into account in their decision-making process. They face questions such as:

  • Which media channels are the most effective?
  • How do these (online or offline) media channels interact with each other?
  • What is the perfect timing for campaigns?
  • How do special events, competitor pricing or external factors affect decisions?

Most marketers will agree that advanced analytical techniques are required to model this complex landscape. However, widely used reporting systems like Google Analytics don’t automatically take these factors into account. As we can see in the following example, this leads to sub-optimal media investment decisions.

Imagine your goal for this month is to maximize the amount of conversions without increasing the total media investment. This requires the reallocation of media budgets among channels. The following data from Google Analytics is available over the previous month:

Campaign Clicks Cost CPC Conversions Conversion Rate Revenue ROI
Display 5.342 € 10.000 € 1,9 1.023 19.1% 100.254 € 10
Facebook 3.253 € 5.000 € 1,5 324 10% 28.512 € 6
AdWords 12.374 € 30.000 € 2,4 1.894 15.3% 164.778 € 5

What would be the optimal re-allocation of media budgets over these channels? The Display channel looks promising due to its high return on investment (ROI) and high conversion rate. It would be perfectly reasonable to assume a higher investment in this channel would lead to the highest revenue. Upon further analysis, however, the opposite could also be true.

In this article, four important lessons that will help to allocate media investments are illustrated using the example above:

1. Use Multi-Touch Attribution (MTA)

When studying the table above, it is important to consider how each of these metrics is calculated. Many systems still report the widely used ‘last click model’. This model assigns the last touchpoint the customer has interacted with all the credit for the conversion, and nothing is attributed to other channels in the customer journey. The last click model therefore overvalues the channels at the end of the customer journey, and undervalues the channels at the beginning of the customer journey. Compared to multi-touch attribution, Google and other search channels are often overvalued using the last click model.

When implementing multi-touch attribution, the first step is to capture all relevant contact points with your customers. Usually, the click journey to the website is used to describe the (online) customer journey. Plotting all these contact points makes it possible to create a more realistic representation of the actual customer journey. The MTA model can then be used to calculate the added value of each channel in the customer journey, taking the order of touchpoints and any interaction between them into account. Using this information, the exact contribution of each individual touchpoint can be calculated. Any additional information on the customer journey or on the individual contact points can be used to improve the MTA model.

In the example above, the type of model is used to calculate the performance metrics is not defined. So, what exactly are we looking at when we see 1.023 conversions for Display?

  • If conversions are calculated using a last click model, the 1.023 conversions in the Display channel indicate Display was the last-clicked channel in a converting customer journey 1.023 times. We cannot conclude that Display was actually responsible for 1.023 conversions.
  • If conversions are calculated using an MTA model, the 1.023 conversions in the Display channel indicate Display is responsible for 1.023 conversions.

In conclusion, it is important to know the meaning of the metrics you are looking at, as well as how they are calculated. For media investment decisions, it is highly recommended to use the more realistic MTA model to attribute conversions.

2. Consider marginal ROI instead of total ROI

In the example, it seems reasonable to shift budget from AdWords to the Display channel, as the Display channel has the highest ROI. However, according to the ‘law of diminishing returns’, the more you invest in a certain channel, the lower the marginal returns of a certain channel. Calculating the marginal ROI is more complex in comparison to calculating ROI, and will require non-linear models logarithmic or s-curved models are often used. In practice, we also find that it is necessary to take other factors into account when calculating investment elasticities, salary weeks and events, for example. (see lesson 3).

Returning to our example, the Display channel could already be relatively saturated, such as at the investment level of €10,000 in the graph above, investing more in display will not give you a ROI of € 10, but a marginal ROI of € 1. Look at the current level of investment in Facebook. This channel is not yet saturated, and has marginal ROI for an investment in Facebook is € 5,7. Graphs like these represent the investment elasticities which are calculated from the data.

In conclusion, making decisions based on marginal ROI instead of on ROI can yield very different results. Create focus within your organization to steer on marginal ROI using investment elasticities.

3. Correct for all factors of influence

Media and sales form a complex landscape with many factors of influence. The example above could yield completely different results

under, for example, a strong price proposition.

Media spend is not the only factor of influence when explaining revenue and sales. Other factors of influence are important to include in predictive models, such as pricing strategy, the weather or holidays. Any relevant information can be included in modeling, even economic trends or events like natural disasters or terrorist attacks could play a role in modeling sales.

It is very important to note that some of these factors, like big launches of new products, interact strongly with your media. Factors that do not interact with media should be included as control variables. Being aware of the interaction and knowing which factors have a strong impact on your channels is vital for making well-thought-out decisions regarding marketing investments.

In conclusion, in order to allocate the media budget optimally, find the factors of influence for your business and incorporate these factors into both MTA- and investment elasticity models.

4. Do not skip levels

Going through the stages above is a complex process, in which many pitfalls can be encountered. It is vital not to skip levels in this process. To understand all the models, to implement them, and to start making decisions based on them in practice should always be considered separate steps.

In order to make models work in practice, it is strongly advised to work towards the final goal using a stepwise approach. Start with the understanding and interpretation of the output of each of the intermediate steps. To prevent models from becoming infamous ‘black box models’, create a plan detailing how to implement the models within the organization. Sharing results with the main stakeholders can be an important aspect of success.

In conclusion, do not skip levels, and make sure you fully understand the models. Create a plan on how to implement the models within the organization.

Arno Witte

Arno Witte

Team Lead Data Science